Document processing optimization

ABSTRACT

There is a need for more effective and efficient document processing solution. Accordingly, various embodiments of the present invention introduce various document processing optimization solutions. In one example, a method includes identifying a plurality of input pages each associated with a related input document of a plurality of input documents; for each input page of the plurality of input pages, generating a segmented page; processing each segmented page using a trained encoder model to generate a fixed-dimensional representation of the input page; determining, based at least in part on each fixed-dimensional representation, a plurality of document clusters; determining a plurality of processing groups, where each processing group is associated with one or more related document clusters of the plurality of document clusters; and performing the document processing optimization based at least in part on the plurality of processing groups.

BACKGROUND

Various embodiments of the present invention address technicalchallenges related to document processing. Various embodiments of thepresent invention disclose innovative techniques for performing documentprocessing by utilizing document processing optimization.

BRIEF SUMMARY

In general, embodiments of the present invention provide methods,apparatus, systems, computing devices, computing entities, and/or thelike for document processing optimization. Certain embodiments utilizesystems, methods, and computer program products that perform documentoptimization by utilizing at least one of page segmentation, documentclusters, page clusters, fixed-dimensional representation of pages, anddocument processing groups.

In accordance with one aspect, a method is provided. In one embodiment,the method comprises identifying a plurality of input pages eachassociated with a related input document of a plurality of inputdocuments; for each input page of the plurality of input pages,generating a segmented page, wherein generating the segmented page for aparticular input page of the plurality of input pages comprises: (i)identifying one or more page segments in the particular input page,wherein each page segment of the one or more page segments is associatedwith a relative location within the particular input page; (ii) for eachpage segment of the one or more page segments, determining a contentpixel density ratio; and (iii) generating the segmented page as a dataobject that describes, for each page segment of the one or more pagesegments, the relative location for the page segment and the contentpixel density ratio for the page segment; processing each segmented pagefor an input page of the plurality of input pages using a trainedencoder model in order to generate a fixed-dimensional representation ofthe input page; determining, based at least in part on eachfixed-dimensional representation for an input page of the plurality ofinput pages, a plurality of document clusters, wherein each documentcluster of the plurality of document clusters comprises a related subsetof the plurality of documents; determining a plurality of processinggroups, wherein: (i) each processing group of the plurality ofprocessing groups is associated with one or more related documentclusters of the plurality of document clusters, (ii) each processinggroup of the plurality of processing groups comprises a subset of theplurality of input documents that is associated with at least one of theone or more related document clusters for the processing group, and(iii) each processing group of the plurality of processing groups isassociated with an assigned processing agent of a plurality ofprocessing agents; and performing the document processing optimizationbased at least in part on the plurality of processing groups.

In accordance with another aspect, a computer program product isprovided. The computer program product may comprise at least onecomputer-readable storage medium having computer-readable program codeportions stored therein, the computer-readable program code portionscomprising executable portions configured to identify a plurality ofinput pages each associated with a related input document of a pluralityof input documents; for each input page of the plurality of input pages,generate a segmented page, wherein generating the segmented page for aparticular input page of the plurality of input pages comprises: (i)identifying one or more page segments in the particular input page,wherein each page segment of the one or more page segments is associatedwith a relative location within the particular input page; (ii) for eachpage segment of the one or more page segments, determining a contentpixel density ratio; and (iii) generating the segmented page as a dataobject that describes, for each page segment of the one or more pagesegments, the relative location for the page segment and the contentpixel density ratio for the page segment; process each segmented pagefor an input page of the plurality of input pages using a trainedencoder model in order to generate a fixed-dimensional representation ofthe input page; determine, based at least in part on eachfixed-dimensional representation for an input page of the plurality ofinput pages, a plurality of document clusters, wherein each documentcluster of the plurality of document clusters comprises a related subsetof the plurality of documents; determining a plurality of processinggroups, wherein: (i) each processing group of the plurality ofprocessing groups is associated with one or more related documentclusters of the plurality of document clusters, (ii) each processinggroup of the plurality of processing groups comprises a subset of theplurality of input documents that is associated with at least one of theone or more related document clusters for the processing group, and(iii) each processing group of the plurality of processing groups isassociated with an assigned processing agent of a plurality ofprocessing agents; and perform the document processing optimizationbased at least in part on the plurality of processing groups.

In accordance with yet another aspect, an apparatus comprising at leastone processor and at least one memory including computer program code isprovided. In one embodiment, the at least one memory and the computerprogram code may be configured to, with the processor, cause theapparatus to identify a plurality of input pages each associated with arelated input document of a plurality of input documents; for each inputpage of the plurality of input pages, generate a segmented page, whereingenerating the segmented page for a particular input page of theplurality of input pages comprises: (i) identifying one or more pagesegments in the particular input page, wherein each page segment of theone or more page segments is associated with a relative location withinthe particular input page; (ii) for each page segment of the one or morepage segments, determining a content pixel density ratio; and (iii)generating the segmented page as a data object that describes, for eachpage segment of the one or more page segments, the relative location forthe page segment and the content pixel density ratio for the pagesegment; process each segmented page for an input page of the pluralityof input pages using a trained encoder model in order to generate afixed-dimensional representation of the input page; determine, based atleast in part on each fixed-dimensional representation for an input pageof the plurality of input pages, a plurality of document clusters,wherein each document cluster of the plurality of document clusterscomprises a related subset of the plurality of documents; determining aplurality of processing groups, wherein: (i) each processing group ofthe plurality of processing groups is associated with one or morerelated document clusters of the plurality of document clusters, (ii)each processing group of the plurality of processing groups comprises asubset of the plurality of input documents that is associated with atleast one of the one or more related document clusters for theprocessing group, and (iii) each processing group of the plurality ofprocessing groups is associated with an assigned processing agent of aplurality of processing agents; and perform the document processingoptimization based at least in part on the plurality of processinggroups.

BRIEF DESCRIPTION OF THE DRAWINGS

Having thus described the invention in general terms, reference will nowbe made to the accompanying drawings, which are not necessarily drawn toscale, and wherein:

FIG. 1 provides an exemplary overview of a hardware architecture thatcan be used to practice embodiments of the present invention.

FIG. 2 provides an example document processing optimization computingentity, in accordance with some embodiments discussed herein.

FIG. 3 provides an example client computing entity, in accordance withsome embodiments discussed herein.

FIG. 4 provides an example agent computing entity, in accordance withsome embodiments discussed herein.

FIG. 5 is a flowchart diagram of an example process for documentprocessing optimization, in accordance with some embodiments discussedherein.

FIG. 6 provides an operational example of division of input documentsinto input pages, in accordance with some embodiments discussed herein.

FIG. 7 is a flowchart of generating a fixed-dimensional representationof an input page, in accordance with some embodiments discussed herein.

FIG. 8 provides an operational example of generating a segmented pagefor an input page, in accordance with some embodiments discussed herein.

FIG. 9 provides an operational example of replacing page segments of asegmented page with segment colors, in accordance with some embodimentsdiscussed herein.

FIG. 10 is a flowchart diagram of an example process for generating atrained encoder model configured to generate fixed-dimensionalrepresentations of input pages, in accordance with some embodimentsdiscussed herein.

FIG. 11 provides an operational example of fixed-dimensionalrepresentations of segmented pages, in accordance with some embodimentsdiscussed herein.

FIG. 12 is a flowchart diagram of an example process for determining aplurality of document clusters based at least in part on cross-documentpage comparisons, in accordance with some embodiments discussed herein.

FIG. 13 is a flowchart diagram of an example process for determining aplurality of document clusters based at least in part on page clusters,in accordance with some embodiments discussed herein.

FIG. 14 is a flowchart diagram of an example process for performingdocument processing using document clusters in accordance with someembodiments discussed herein.

FIG. 15 is an operational diagram of an example process for determiningprocessing groups, in accordance with some embodiments discussed herein.

DETAILED DESCRIPTION

Various embodiments of the present invention now will be described morefully hereinafter with reference to the accompanying drawings, in whichsome, but not all embodiments of the inventions are shown. Indeed, theseinventions may be embodied in many different forms and should not beconstrued as limited to the embodiments set forth herein; rather, theseembodiments are provided so that this disclosure will satisfy applicablelegal requirements. The term “or” is used herein in both the alternativeand conjunctive sense, unless otherwise indicated. The terms“illustrative” and “exemplary” are used to be examples with noindication of quality level. Like numbers refer to like elementsthroughout. Moreover, one of ordinary skill in the art will recognizethat the disclosed concepts can be used to perform other types of dataanalysis.

I. OVERVIEW

Various embodiments of the present invention provide techniques forincreasing efficiency and reliability of document processing systems bydynamically separating a batch of documents into processing groups basedat least in part on structural similarity of the pages of the noteddocuments. Absent this dynamic separation process, processing ofdocuments by processing agents (e.g., automated processing agents) maybe less efficient and less accurate, as the processing agents will beless likely to capture cross-temporal expertise acquired from repeatedlyprocessing input documents having similar structural formats. This inturn reduces the overall operational bandwidth and overall operationalreliability of a multi-agent distributed document processing system.Accordingly, by dynamically separating a batch of documents intoprocessing groups based at least in part on structural similarity of thepages of the noted documents, various embodiments of the presentinvention make important technical contributions to efficiency andreliability of document processing systems, and substantially improveoverall operational bandwidth and overall operational reliability ofexisting multi-agent distributed document processing systems.

Moreover, various embodiments of the present invention make importanttechnical contributions to the field of document clustering byintroducing techniques for integrating page-level similarity data ininferring document clusters. For example, various embodiments of thepresent invention introduce techniques for determining whether twodocuments should be included in the same document cluster based at leastin part on a count of sufficiently similar pages between the twodocuments. As another example, various embodiments of the presentinvention introduce techniques for determining whether two documentsshould be included in the same document cluster based at least in parton a ratio of pages of the two documents that are deemed to be within acommon page cluster. As page-level similarity analyses are oftencomputationally less resource-intensive than document-level similarityanalyses, the noted techniques for integrating page-level similaritydata in inferring document clusters can reduce the computational cost ofdocument clustering. Accordingly, by introducing techniques forintegrating page-level similarity data in inferring document clusterscan reduce the computational cost of document clustering, variousembodiments of the present invention make important technicalcontributions to improving computational efficiency of documentclustering systems.

II. DEFINITIONS

An “input document” may be a data object that describes a collection ofcontent data, such as a collection of text data and/or a collection ofimage data. Examples of the input documents include Portable DocumentFormat (PDF) documents, Tagged Image File Format (TIFF) documents,Microsoft Word documents, and/or the like. Each input document includesone or more input pages.

An “input page” may be a data object that describes a visuallycontiguous portion of an input document. In some embodiments, an inputdocument is stored as a collection of input pages. Input pages of aninput document may be defined based at least in part on page divisiondata for the input document, where the page division data may define apage association for each content item associated with the inputdocument.

An “input page” segment may be data object that describes a collectionof pixels of a corresponding input page that are deemed to have contentsof a common format (e.g., a common text format, a common image format, acommon text format with a common font and a common size, and/or thelike). In some embodiments, a computer system is configured to processan input page in order to generate a segmented page, which may be a dataobject that describes one or more input page segments identified bycomputer system to be associated with the input page.

A “training page” may be a data object that describes an input page thatis configured to be used for training an encoder model, where theencoder model is configured to be utilized to generate fixed-dimensionalrepresentations of input pages. During training, a batch training pagesmay be processed by the encoder model to generate a fixed-dimensionalrepresentation of each training page in the batch of training pages.Afterward, the fixed-dimensional representations of the batch oftraining pages may be processed by a decoder model to generate areconstructed page for each training page in the batch of trainingpages. The parameters of the encoder model may then be updated inaccordance with a measure of deviation between reconstructed pages andthe training pages across the batch of training pages.

A “fixed-dimensional representation” for a corresponding input page maybe a data object that describes a vector generated by an encoder modelafter processing the corresponding input page based at least in part onthe parameters of the encoder model. During training of the encodermodel, the fixed-dimensional representation of a training page may beprocessed by a decoder model to generate a reconstructed page, where thedeviation between the training page and the reconstructed page isutilized to update the parameters of the encoder mode. After deploymentof a trained encoder model, the fixed-dimensional representation of aninput page may be used to generate document clusters that are in turnused to generate processing groups for utilization by documentprocessing agents.

A “cross-page similarity score” may be a data object that describes anestimated degree of similarity between a corresponding pair of inputpages. For example, a cross-page similarity score for a first pair ofpages having a more similar layout may be higher than a cross-pagesimilarity score for a second pair of pages having a less similarlayout.

A “similar-page count” may be a data object that describes a number ofsufficiently-similar page pairs associated with a corresponding pair ofinput documents, where a sufficiently-similar page pair associated withthe corresponding pair of input documents is a pair of input pagesincluding a first page from a first input document of the pair of inputdocuments and a second input page from a second input document of thepair of input documents with the cross-page similarity between the pairof input pages exceeding a cross-page similarity threshold. For example,given a first input document including two input pages P1 and P2 and asecond input document including two input pages P3 and P4, if thecross-page similarity score for P1 and P3 is forty percent, thecross-page similarity score for P1 and P4 is sixty percent, thecross-page similarity score for P2 and P3 is seventy percent, and thecross-page similarity score for P2 and P4 is forty percent, and furtherif the cross-page similarity score threshold is fifty percent, thesufficiently-similarity page pairs associated with the two inputdocuments include P1 and P4 as well as P2 and P3, and the similar-pagecount for the two input documents is two.

A “page cluster” may be a data object that describes a group of inputpages deemed to be have a sufficiently similar input page layout. Insome embodiments, to generate a group of page clusters for a group ofinput pages, a computer system processes each fixed-dimensionalrepresentation for an input page in the group of input pages usingt-distributed stochastic neighbor embedding in order to generate areduced-dimensional representation for each input page, and generatesthe group of page clusters based at least in part on eachreduced-dimensional representation for an input page in the group ofinput pages.

A “document cluster” may be a data object that describes a group ofinput documents whose input pages are deemed to have sufficient inputlayout similarity with each other. In some embodiments, to generate agroup of document clusters for a group of input documents, a computersystem generates a group of page clusters for the input pages of thegroup of input documents, and subsequently clusters the input documentsbased at least in part on a degree of relatedness between each documentand each page cluster. For example, if ninety percent of the input pagesfor a first input document fall within a first page cluster, thecomputer system may cluster the first input document in a documentcluster that includes all input documents deemed to be sufficientlyrelated to the first page cluster. In some embodiments, a computersystem clusters two input documents as part of a common document clusterif a measure of the similar-page count for the two input documentsexceeds a threshold measure.

A “processing group” may be a data object that describes a group ofdocument clusters that are collectively assigned to a processing agentfor document processing. Accordingly, a processing group includes eachinput document that is in at least one of the group of document clustersthat is associated with the processing group. In some embodiments, togenerate a group of processing groups based at least in part on a groupof document clusters, a computer system randomly divides the group ofdocument clusters into two or more document cluster subdivisions,assigns each document cluster subdivision to a processing group, andassigns each processing group to a processing agent based at least inpart on an operational capacity of the processing agent. For example, ifa processing group includes ten input documents, the computer systemassigns the processing group to a processing agent whose operationalcapacity is ten or more. In some embodiments, to generate a group ofprocessing groups based at least in part on a group of documentclusters, a computer system divides the group of document clusters intotwo or more document cluster subdivisions based at least in part onmeasures of relatedness between each document cluster pairs, where eachdocument cluster pair includes two document clusters of the group ofdocument clusters. For example, to generate a measure of relatednessbetween a pair of document clusters, the computer system may randomlyselect a first input document from a first document cluster in the pairof document clusters and a second input document from a second documentcluster in the pair of document clusters, determine a measure ofcross-document similarity of the first input document and the secondinput document (e.g., based at least in part on a similar-page count ofthe first input document and the second input document), and generatethe measure of relatedness based at least in part on the measure ofcross-document similarity of the first input document and the secondinput document.

III. COMPUTER PROGRAM PRODUCTS, METHODS, AND COMPUTING ENTITIES

Embodiments of the present invention may be implemented in various ways,including as computer program products that comprise articles ofmanufacture. Such computer program products may comprise one or moresoftware components including, for example, software objects, methods,data structures, or the like. A software component may be coded in anyof a variety of programming languages. An illustrative programminglanguage may be a lower-level programming language such as an assemblylanguage associated with a particular hardware architecture and/oroperating system platform. A software component comprising assemblylanguage instructions may require conversion into executable machinecode by an assembler prior to execution by the hardware architectureand/or platform. Another example programming language may be ahigher-level programming language that may be portable across multiplearchitectures. A software component comprising higher-level programminglanguage instructions may require conversion to an intermediaterepresentation by an interpreter or a compiler prior to execution.

Other examples of programming languages include, but are not limited to,a macro language, a shell or command language, a job control language, ascript language, a database query or search language, and/or a reportwriting language. In one or more example embodiments, a softwarecomponent comprising instructions in one of the foregoing examples ofprogramming languages may be executed directly by an operating system orother software component without having to be first transformed intoanother form. A software component may be stored as a file or other datastorage construct. Software components of a similar type or functionallyrelated may be stored together such as, for example, in a particulardirectory, folder, or library. Software components may be static (e.g.,pre-established or fixed) or dynamic (e.g., created or modified at thetime of execution).

A computer program product may comprise a non-transitorycomputer-readable storage medium storing applications, programs, programmodules, scripts, source code, program code, object code, byte code,compiled code, interpreted code, machine code, executable instructions,and/or the like (also referred to herein as executable instructions,instructions for execution, computer program products, program code,and/or similar terms used herein interchangeably). Such non-transitorycomputer-readable storage media comprise all computer-readable media(including volatile and non-volatile media).

In one embodiment, a non-volatile computer-readable storage medium maycomprise a floppy disk, flexible disk, hard disk, solid-state storage(SSS) (e.g., a solid state drive (SSD), solid state card (SSC), solidstate module (SSM), enterprise flash drive, magnetic tape, or any othernon-transitory magnetic medium, and/or the like. A non-volatilecomputer-readable storage medium may also comprise a punch card, papertape, optical mark sheet (or any other physical medium with patterns ofholes or other optically recognizable indicia), compact disc read onlymemory (CD-ROM), compact disc-rewritable (CD-RW), digital versatile disc(DVD), Blu-ray disc (BD), any other non-transitory optical medium,and/or the like. Such a non-volatile computer-readable storage mediummay also comprise read-only memory (ROM), programmable read-only memory(PROM), erasable programmable read-only memory (EPROM), electricallyerasable programmable read-only memory (EEPROM), flash memory (e.g.,Serial, NAND, NOR, and/or the like), multimedia memory cards (MMC),secure digital (SD) memory cards, SmartMedia cards, CompactFlash (CF)cards, Memory Sticks, and/or the like. Further, a non-volatilecomputer-readable storage medium may also comprise conductive-bridgingrandom access memory (CBRAM), phase-change random access memory (PRAM),ferroelectric random-access memory (FeRAM), non-volatile random-accessmemory (NVRAM), magneto-resistive random-access memory (MRAM), resistiverandom-access memory (RRAM), Silicon-Oxide-Nitride-Oxide-Silicon memory(SONOS), floating junction gate random access memory (FJG RAM),Millipede memory, racetrack memory, and/or the like.

In one embodiment, a volatile computer-readable storage medium maycomprise random access memory (RAM), dynamic random access memory(DRAM), static random access memory (SRAM), fast page mode dynamicrandom access memory (FPM DRAM), extended data-out dynamic random accessmemory (EDO DRAM), synchronous dynamic random access memory (SDRAM),double data rate synchronous dynamic random access memory (DDR SDRAM),double data rate type two synchronous dynamic random access memory (DDR2SDRAM), double data rate type three synchronous dynamic random accessmemory (DDR3 SDRAM), Rambus dynamic random access memory (RDRAM), TwinTransistor RAM (TTRAM), Thyristor RAM (T-RAM), Zero-capacitor (Z-RAM),Rambus in-line memory module (RIMM), dual in-line memory module (DIMM),single in-line memory module (SIMM), video random access memory (VRAM),cache memory (including various levels), flash memory, register memory,and/or the like. It will be appreciated that where embodiments aredescribed to use a computer-readable storage medium, other types ofcomputer-readable storage media may be substituted for or used inaddition to the computer-readable storage media described above.

As should be appreciated, various embodiments of the present inventionmay also be implemented as methods, apparatus, systems, computingdevices, computing entities, and/or the like. As such, embodiments ofthe present invention may take the form of an apparatus, system,computing device, computing entity, and/or the like executinginstructions stored on a computer-readable storage medium to performcertain steps or operations. Thus, embodiments of the present inventionmay also take the form of an entirely hardware embodiment, an entirelycomputer program product embodiment, and/or an embodiment that comprisescombination of computer program products and hardware performing certainsteps or operations.

Embodiments of the present invention are described below with referenceto block diagrams and flowchart illustrations. Thus, it should beunderstood that each block of the block diagrams and flowchartillustrations may be implemented in the form of a computer programproduct, an entirely hardware embodiment, a combination of hardware andcomputer program products, and/or apparatus, systems, computing devices,computing entities, and/or the like carrying out instructions,operations, steps, and similar words used interchangeably (e.g., theexecutable instructions, instructions for execution, program code,and/or the like) on a computer-readable storage medium for execution.For example, retrieval, loading, and execution of code may be performedsequentially such that one instruction is retrieved, loaded, andexecuted at a time. In some exemplary embodiments, retrieval, loading,and/or execution may be performed in parallel such that multipleinstructions are retrieved, loaded, and/or executed together. Thus, suchembodiments can produce specifically-configured machines performing thesteps or operations specified in the block diagrams and flowchartillustrations. Accordingly, the block diagrams and flowchartillustrations support various combinations of embodiments for performingthe specified instructions, operations, or steps.

IV. EXEMPLARY SYSTEM ARCHITECTURE

FIG. 1 is a schematic diagram of an example architecture 100 fordocument processing optimization. The architecture 100 comprises adocument processing optimization system 101, one or more clientcomputing entities 102, and one or more agent computing entities 103.The document processing optimization system 101 is configured togenerate document processing groups, provide document processing groupsto the agent computing entities 103, receive document processing outputsfrom the agent computing entities 103, and provide the documentprocessing outputs to the client computing entities 102.

In some embodiments, the document processing optimization system 101 maycommunicate with the client computing entities 102 and/or the agentcomputing entities 103 using one or more communication networks.Examples of communication networks comprise any wired or wirelesscommunication network including, for example, a wired or wireless localarea network (LAN), personal area network (PAN), metropolitan areanetwork (MAN), wide area network (WAN), or the like, as well as anyhardware, software and/or firmware required to implement it (such as,e.g., network routers, and/or the like).

The document processing optimization system 101 may comprise a documentprocessing optimization computing entity 106 and a storage subsystem108. The document processing optimization computing entity 106 may beconfigured to generate document processing groups based at least in parton a group of input documents. The storage subsystem 108 may beconfigured to store input documents used by the document processingoptimization computing entity 106 to perform document processingoptimization. The storage subsystem 108 may further be configured tostore model definition information/data for document processingoptimization models used by the document processing optimizationcomputing entity 106 to perform document processing optimization.

The storage subsystem 108 may comprise one or more storage units, suchas multiple distributed storage units that are connected through acomputer network. Each storage unit in the storage subsystem 108 maystore at least one of one or more information/data assets and/or one ormore information/data about the computed properties of one or moreinformation/data assets. Moreover, each storage unit in the storagesubsystem 108 may comprise one or more non-volatile storage or memorymedia including but not limited to hard disks, ROM, PROM, EPROM, EEPROM,flash memory, MMCs, SD memory cards, Memory Sticks, CBRAM, PRAM, FeRAM,NVRAM, MRAM, RRAM, SONOS, FJG RAM, Millipede memory, racetrack memory,and/or the like.

Exemplary Document Processing Optimization Computing Entity

FIG. 2 provides a schematic of a document processing optimizationcomputing entity 106 according to one embodiment of the presentinvention. In general, the terms computing entity, computer, entity,device, system, and/or similar words used herein interchangeably mayrefer to, for example, one or more computers, computing entities,desktops, mobile phones, tablets, phablets, notebooks, laptops,distributed systems, kiosks, input terminals, servers or servernetworks, blades, gateways, switches, processing devices, processingentities, set-top boxes, relays, routers, network access points, basestations, the like, and/or any combination of devices or entitiesadapted to perform the functions, operations, and/or processes describedherein. Such functions, operations, and/or processes may include, forexample, transmitting, receiving, operating on, processing, displaying,storing, determining, creating/generating, monitoring, evaluating,comparing, and/or similar terms used herein interchangeably. In oneembodiment, these functions, operations, and/or processes can beperformed on data, content, information, and/or similar terms usedherein interchangeably.

As indicated, in one embodiment, the document processing optimizationcomputing entity 106 may also comprise one or more network interfaces220 for communicating with various computing entities, such as bycommunicating data, content, information, and/or similar terms usedherein interchangeably that can be transmitted, received, operated on,processed, displayed, stored, and/or the like.

As shown in FIG. 2, in one embodiment, the document processingoptimization computing entity 106 may comprise or be in communicationwith one or more processing elements 205 (also referred to asprocessors, processing circuitry, and/or similar terms used hereininterchangeably) that communicate with other elements within thedocument processing optimization computing entity 106 via a bus, forexample. As will be understood, the processing element 205 may beembodied in a number of different ways.

For example, the processing element 205 may be embodied as one or morecomplex programmable logic devices (CPLDs), microprocessors, multi-coreprocessors, coprocessing entities, application-specific instruction-setprocessors (ASIPs), microcontrollers, and/or controllers. Further, theprocessing element 205 may be embodied as one or more other processingdevices or circuitry. The term circuitry may refer to an entirelyhardware embodiment or a combination of hardware and computer programproducts. Thus, the processing element 205 may be embodied as integratedcircuits, application specific integrated circuits (ASICs), fieldprogrammable gate arrays (FPGAs), programmable logic arrays (PLAs),hardware accelerators, another circuitry, and/or the like.

As will therefore be understood, the processing element 205 may beconfigured for a particular use or configured to execute instructionsstored in volatile or non-volatile media or otherwise accessible to theprocessing element 205. As such, whether configured by hardware orcomputer program products, or by a combination thereof, the processingelement 205 may be capable of performing steps or operations accordingto embodiments of the present invention when configured accordingly.

In one embodiment, the document processing optimization computing entity106 may further comprise or be in communication with non-volatile media(also referred to as non-volatile storage, memory, memory storage,memory circuitry and/or similar terms used herein interchangeably). Inone embodiment, the non-volatile storage or memory may comprise one ormore non-volatile storage or memory media 210, including but not limitedto hard disks, ROM, PROM, EPROM, EEPROM, flash memory, MMCs, SD memorycards, Memory Sticks, CBRAM, PRAM, FeRAM, NVRAM, MRAM, RRAM, SONOS, FJGRAM, Millipede memory, racetrack memory, and/or the like.

As will be recognized, the non-volatile storage or memory media maystore databases, database instances, database management systems, data,applications, programs, program modules, scripts, source code, objectcode, byte code, compiled code, interpreted code, machine code,executable instructions, and/or the like. The term database, databaseinstance, database management system, and/or similar terms used hereininterchangeably may refer to a collection of records or information/datathat is stored in a computer-readable storage medium using one or moredatabase models, such as a hierarchical database model, network model,relational model, entity—relationship model, object model, documentmodel, semantic model, graph model, and/or the like.

In one embodiment, the document processing optimization computing entity106 may further comprise or be in communication with volatile media(also referred to as volatile storage, memory, memory storage, memorycircuitry and/or similar terms used herein interchangeably). In oneembodiment, the volatile storage or memory may also comprise one or morevolatile storage or memory media 215, including but not limited to RAM,DRAM, SRAM, FPM DRAM, EDO DRAM, SDRAM, DDR SDRAM, DDR2 SDRAM, DDR3SDRAM, RDRAM, TTRAM, T-RAM, Z-RAM, RIMM, DIMM, SIMM, VRAM, cache memory,register memory, and/or the like.

As will be recognized, the volatile storage or memory media may be usedto store at least portions of the databases, database instances,database management systems, data, applications, programs, programmodules, scripts, source code, object code, byte code, compiled code,interpreted code, machine code, executable instructions, and/or the likebeing executed by, for example, the processing element 205. Thus, thedatabases, database instances, database management systems, data,applications, programs, program modules, scripts, source code, objectcode, byte code, compiled code, interpreted code, machine code,executable instructions, and/or the like may be used to control certainaspects of the operation of the document processing optimizationcomputing entity 106 with the assistance of the processing element 205and operating system.

As indicated, in one embodiment, the document processing optimizationcomputing entity 106 may also comprise one or more communicationsinterfaces 220 for communicating with various computing entities, suchas by communicating data, content, information, and/or similar termsused herein interchangeably that can be transmitted, received, operatedon, processed, displayed, stored, and/or the like. Such communicationmay be executed using a wired data transmission protocol, such as fiberdistributed data interface (FDDI), digital subscriber line (DSL),Ethernet, asynchronous transfer mode (ATM), frame relay, data over cableservice interface specification (DOCSIS), or any other wiredtransmission protocol. Similarly, the document processing optimizationcomputing entity 106 may be configured to communicate via wirelessclient communication networks using any of a variety of protocols, suchas general packet radio service (GPRS), Universal MobileTelecommunications System (UMTS), Code Division Multiple Access 2000(CDMA2000), CDMA2000 1× (1×RTT), Wideband Code Division Multiple Access(WCDMA), Global System for Mobile Communications (GSM), Enhanced Datarates for GSM Evolution (EDGE), Time Division-Synchronous Code DivisionMultiple Access (TD-SCDMA), Long Term Evolution (LTE), Evolved UniversalTerrestrial Radio Access Network (E-UTRAN), Evolution-Data Optimized(EVDO), High Speed Packet Access (HSPA), High-Speed Downlink PacketAccess (HSDPA), IEEE 802.11 (Wi-Fi), Wi-Fi Direct, 802.16 (WiMAX),ultra-wideband (UWB), infrared (IR) protocols, near field communication(NFC) protocols, Wibree, Bluetooth protocols, wireless universal serialbus (USB) protocols, and/or any other wireless protocol.

Although not shown, the document processing optimization computingentity 106 may comprise or be in communication with one or more inputelements, such as a keyboard input, a mouse input, a touchscreen/display input, motion input, movement input, audio input,pointing device input, joystick input, keypad input, and/or the like.The document processing optimization computing entity 106 may alsocomprise or be in communication with one or more output elements (notshown), such as audio output, video output, screen/display output,motion output, movement output, and/or the like.

Exemplary Client Computing Entity

FIG. 3 provides an illustrative schematic representative of a clientcomputing entity 102 that can be used in conjunction with embodiments ofthe present invention. In general, the terms device, system, computingentity, entity, and/or similar words used herein interchangeably mayrefer to, for example, one or more computers, computing entities,desktops, mobile phones, tablets, phablets, notebooks, laptops,distributed systems, kiosks, input terminals, servers or servernetworks, blades, gateways, switches, processing devices, processingentities, set-top boxes, relays, routers, network access points, basestations, the like, and/or any combination of devices or entitiesadapted to perform the functions, operations, and/or processes describedherein. Client computing entities 102 can be operated by variousparties. As shown in FIG. 3, the client computing entity 102 cancomprise an antenna 312, a transmitter 304 (e.g., radio), a receiver 306(e.g., radio), and a processing element 308 (e.g., CPLDs,microprocessors, multi-core processors, coprocessing entities, ASIPs,microcontrollers, and/or controllers) that provides signals to andreceives signals from the transmitter 304 and receiver 306,correspondingly.

The signals provided to and received from the transmitter 304 and thereceiver 306, correspondingly, may comprise signaling information/datain accordance with air interface standards of applicable wirelesssystems. In this regard, the client computing entity 102 may be capableof operating with one or more air interface standards, communicationprotocols, modulation types, and access types. More particularly, theclient computing entity 102 may operate in accordance with any of anumber of wireless communication standards and protocols, such as thosedescribed above with regard to the document processing optimizationcomputing entity 106. In a particular embodiment, the client computingentity 102 may operate in accordance with multiple wirelesscommunication standards and protocols, such as UMTS, CDMA2000, 1×RTT,WCDMA, GSM, EDGE, TD-SCDMA, LTE, E-UTRAN, EVDO, HSPA, HSDPA, Wi-Fi,Wi-Fi Direct, WiMAX, UWB, IR, NFC, Bluetooth, USB, and/or the like.Similarly, the client computing entity 102 may operate in accordancewith multiple wired communication standards and protocols, such as thosedescribed above with regard to the document processing optimizationcomputing entity 106 via a network interface 320.

Via these communication standards and protocols, the client computingentity 102 can communicate with various other entities using conceptssuch as Unstructured Supplementary Service Data (USSD), Short MessageService (SMS), Multimedia Messaging Service (MIMS), Dual-ToneMulti-Frequency Signaling (DTMF), and/or Subscriber Identity ModuleDialer (SIM dialer). The client computing entity 102 can also downloadchanges, add-ons, and updates, for instance, to its firmware, software(e.g., including executable instructions, applications, programmodules), and operating system.

According to one embodiment, the client computing entity 102 maycomprise location determining aspects, devices, modules,functionalities, and/or similar words used herein interchangeably. Forexample, the client computing entity 102 may comprise outdoorpositioning aspects, such as a location module adapted to acquire, forexample, latitude, longitude, altitude, geocode, course, direction,heading, speed, universal time (UTC), date, and/or various otherinformation/data. In one embodiment, the location module can acquiredata, sometimes known as ephemeris data, by identifying the number ofsatellites in view and the relative positions of those satellites (e.g.,using global positioning systems (GPS)). The satellites may be a varietyof different satellites, including Low Earth Orbit (LEO) satellitesystems, Department of Defense (DOD) satellite systems, the EuropeanUnion Galileo positioning systems, the Chinese Compass navigationsystems, Indian Regional Navigational satellite systems, and/or thelike. This information/data can be collected using a variety ofcoordinate systems, such as the Decimal Degrees (DD); Degrees, Minutes,Seconds (DMS); Universal Transverse Mercator (UTM); Universal PolarStereographic (UPS) coordinate systems; and/or the like. Alternatively,the location information/data can be determined by triangulating theclient computing entity's 102 position in connection with a variety ofother systems, including cellular towers, Wi-Fi access points, and/orthe like. Similarly, the client computing entity 102 may comprise indoorpositioning aspects, such as a location module adapted to acquire, forexample, latitude, longitude, altitude, geocode, course, direction,heading, speed, time, date, and/or various other information/data. Someof the indoor systems may use various position or location technologiesincluding RFID tags, indoor beacons or transmitters, Wi-Fi accesspoints, cellular towers, nearby computing devices (e.g., smartphones,laptops) and/or the like. For instance, such technologies may comprisethe iBeacons, Gimbal proximity beacons, Bluetooth Low Energy (BLE)transmitters, NFC transmitters, and/or the like. These indoorpositioning aspects can be used in a variety of settings to determinethe location of someone or something to within inches or centimeters.

The client computing entity 102 may also comprise a user interface (thatcan comprise a display 316 coupled to a processing element 308) and/or auser input interface (coupled to a processing element 308). For example,the user interface may be a user application, browser, user interface,and/or similar words used herein interchangeably executing on and/oraccessible via the client computing entity 102 to interact with and/orcause display of information/data from the document processingoptimization computing entity 106, as described herein. The user inputinterface can comprise any of a number of devices or interfaces allowingthe client computing entity 102 to receive data, such as a keypad 318(hard or soft), a touch display, voice/speech or motion interfaces, orother input device. In embodiments including a keypad 318, the keypad318 can comprise (or cause display of) the conventional numeric (0-9)and related keys (#, *), and other keys used for operating the clientcomputing entity 102 and may comprise a full set of alphabetic keys orset of keys that may be activated to provide a full set of alphanumerickeys. In addition to providing input, the user input interface can beused, for example, to activate or deactivate certain functions, such asscreen savers and/or sleep modes.

The client computing entity 102 can also comprise volatile storage ormemory 322 and/or non-volatile storage or memory 324, which can beembedded and/or may be removable. For example, the non-volatile memorymay be ROM, PROM, EPROM, EEPROM, flash memory, MMCs, SD memory cards,Memory Sticks, CBRAM, PRAM, FeRAM, NVRAM, MRAM, RRAM, SONOS, FJG RAM,Millipede memory, racetrack memory, and/or the like. The volatile memorymay be RAM, DRAM, SRAM, FPM DRAM, EDO DRAM, SDRAM, DDR SDRAM, DDR2SDRAM, DDR3 SDRAM, RDRAM, TTRAM, T-RAM, Z-RAM, RIMM, DIMM, SIMM, VRAM,cache memory, register memory, and/or the like. The volatile andnon-volatile storage or memory can store databases, database instances,database management systems, data, applications, programs, programmodules, scripts, source code, object code, byte code, compiled code,interpreted code, machine code, executable instructions, and/or the liketo implement the functions of the client computing entity 102. Asindicated, this may comprise a user application that is resident on theentity or accessible through a browser or other user interface forcommunicating with the document processing optimization computing entity106 and/or various other computing entities.

In another embodiment, the client computing entity 102 may comprise oneor more components or functionality that are the same or similar tothose of the document processing optimization computing entity 106, asdescribed in greater detail above. As will be recognized, thesearchitectures and descriptions are provided for exemplary purposes onlyand are not limiting to the various embodiments.

In various embodiments, the client computing entity 102 may be embodiedas an artificial intelligence (AI) computing entity, such as an AmazonEcho, Amazon Echo Dot, Amazon Show, Google Home, and/or the like.Accordingly, the client computing entity 102 may be configured toprovide and/or receive information/data from a user via an input/outputmechanism, such as a display, a camera, a speaker, a voice-activatedinput, and/or the like. In certain embodiments, an AI computing entitymay comprise one or more predefined and executable program algorithmsstored within an onboard memory storage module, and/or accessible over anetwork. In various embodiments, the AI computing entity may beconfigured to retrieve and/or execute one or more of the predefinedprogram algorithms upon the occurrence of a predefined trigger event.

Exemplary Agent Computing Entity

FIG. 4 provides an illustrative schematic representative of an agentcomputing entity 103 that can be used in conjunction with embodiments ofthe present invention. In general, the terms device, system, computingentity, entity, and/or similar words used herein interchangeably mayrefer to, for example, one or more computers, computing entities,desktops, mobile phones, tablets, phablets, notebooks, laptops,distributed systems, kiosks, input terminals, servers or servernetworks, blades, gateways, switches, processing devices, processingentities, set-top boxes, relays, routers, network access points, basestations, the like, and/or any combination of devices or entitiesadapted to perform the functions, operations, and/or processes describedherein. Agent computing entities 103 can be operated by various parties.As shown in FIG. 4, the agent computing entity 103 can comprise anantenna 412, a transmitter 404 (e.g., radio), a receiver 406 (e.g.,radio), and a processing element 408 (e.g., CPLDs, microprocessors,multi-core processors, coprocessing entities, ASIPs, microcontrollers,and/or controllers) that provides signals to and receives signals fromthe transmitter 404 and receiver 406, correspondingly.

The signals provided to and received from the transmitter 404 and thereceiver 406, correspondingly, may comprise signaling information/datain accordance with air interface standards of applicable wirelesssystems. In this regard, the agent computing entity 103 may be capableof operating with one or more air interface standards, communicationprotocols, modulation types, and access types. More particularly, theagent computing entity 103 may operate in accordance with any of anumber of wireless communication standards and protocols, such as thosedescribed above with regard to the document processing optimizationcomputing entity 106. In a particular embodiment, the agent computingentity 103 may operate in accordance with multiple wirelesscommunication standards and protocols, such as UMTS, CDMA2000, 1×RTT,WCDMA, GSM, EDGE, TD-SCDMA, LTE, E-UTRAN, EVDO, HSPA, HSDPA, Wi-Fi,Wi-Fi Direct, WiMAX, UWB, IR, NFC, Bluetooth, USB, and/or the like.Similarly, the agent computing entity 103 may operate in accordance withmultiple wired communication standards and protocols, such as thosedescribed above with regard to the document processing optimizationcomputing entity 106 via a network interface 420.

Via these communication standards and protocols, the agent computingentity 103 can communicate with various other entities using conceptssuch as Unstructured Supplementary Service Data (USSD), Short MessageService (SMS), Multimedia Messaging Service (MIMS), Dual-ToneMulti-Frequency Signaling (DTMF), and/or Subscriber Identity ModuleDialer (SIM dialer). The agent computing entity 103 can also downloadchanges, add-ons, and updates, for instance, to its firmware, software(e.g., including executable instructions, applications, programmodules), and operating system.

According to one embodiment, the agent computing entity 103 may compriselocation determining aspects, devices, modules, functionalities, and/orsimilar words used herein interchangeably. For example, the agentcomputing entity 103 may comprise outdoor positioning aspects, such as alocation module adapted to acquire, for example, latitude, longitude,altitude, geocode, course, direction, heading, speed, universal time(UTC), date, and/or various other information/data. In one embodiment,the location module can acquire data, sometimes known as ephemeris data,by identifying the number of satellites in view and the relativepositions of those satellites (e.g., using global positioning systems(GPS)). The satellites may be a variety of different satellites,including Low Earth Orbit (LEO) satellite systems, Department of Defense(DOD) satellite systems, the European Union Galileo positioning systems,the Chinese Compass navigation systems, Indian Regional Navigationalsatellite systems, and/or the like. This information/data can becollected using a variety of coordinate systems, such as the DecimalDegrees (DD); Degrees, Minutes, Seconds (DMS); Universal TransverseMercator (UTM); Universal Polar Stereographic (UPS) coordinate systems;and/or the like. Alternatively, the location information/data can bedetermined by triangulating the agent computing entity's 103 position inconnection with a variety of other systems, including cellular towers,Wi-Fi access points, and/or the like. Similarly, the agent computingentity 103 may comprise indoor positioning aspects, such as a locationmodule adapted to acquire, for example, latitude, longitude, altitude,geocode, course, direction, heading, speed, time, date, and/or variousother information/data. Some of the indoor systems may use variousposition or location technologies including RFID tags, indoor beacons ortransmitters, Wi-Fi access points, cellular towers, nearby computingdevices (e.g., smartphones, laptops) and/or the like. For instance, suchtechnologies may comprise the iBeacons, Gimbal proximity beacons,Bluetooth Low Energy (BLE) transmitters, NFC transmitters, and/or thelike. These indoor positioning aspects can be used in a variety ofsettings to determine the location of someone or something to withininches or centimeters.

The agent computing entity 103 may also comprise a user interface (thatcan comprise a display 416 coupled to a processing element 408) and/or auser input interface (coupled to a processing element 408). For example,the user interface may be a user application, browser, user interface,and/or similar words used herein interchangeably executing on and/oraccessible via the agent computing entity 103 to interact with and/orcause display of information/data from the document processingoptimization computing entity 106, as described herein. The user inputinterface can comprise any of a number of devices or interfaces allowingthe agent computing entity 103 to receive data, such as a keypad 418(hard or soft), a touch display, voice/speech or motion interfaces, orother input device. In embodiments including a keypad 418, the keypad418 can comprise (or cause display of) the conventional numeric (0-9)and related keys (#, *), and other keys used for operating the agentcomputing entity 103 and may comprise a full set of alphabetic keys orset of keys that may be activated to provide a full set of alphanumerickeys. In addition to providing input, the user input interface can beused, for example, to activate or deactivate certain functions, such asscreen savers and/or sleep modes.

The agent computing entity 103 can also comprise volatile storage ormemory 422 and/or non-volatile storage or memory 424, which can beembedded and/or may be removable. For example, the non-volatile memorymay be ROM, PROM, EPROM, EEPROM, flash memory, MMCs, SD memory cards,Memory Sticks, CBRAM, PRAM, FeRAM, NVRAM, MRAM, RRAM, SONOS, FJG RAM,Millipede memory, racetrack memory, and/or the like. The volatile memorymay be RAM, DRAM, SRAM, FPM DRAM, EDO DRAM, SDRAM, DDR SDRAM, DDR2SDRAM, DDR3 SDRAM, RDRAM, TTRAM, T-RAM, Z-RAM, RIMM, DIMM, SIMM, VRAM,cache memory, register memory, and/or the like. The volatile andnon-volatile storage or memory can store databases, database instances,database management systems, data, applications, programs, programmodules, scripts, source code, object code, byte code, compiled code,interpreted code, machine code, executable instructions, and/or the liketo implement the functions of the agent computing entity 103. Asindicated, this may comprise a user application that is resident on theentity or accessible through a browser or other user interface forcommunicating with the document processing optimization computing entity106 and/or various other computing entities.

In another embodiment, the agent computing entity 103 may comprise oneor more components or functionality that are the same or similar tothose of the document processing optimization computing entity 106, asdescribed in greater detail above. As will be recognized, thesearchitectures and descriptions are provided for exemplary purposes onlyand are not limiting to the various embodiments.

In various embodiments, the agent computing entity 103 may be embodiedas an artificial intelligence (AI) computing entity, such as an AmazonEcho, Amazon Echo Dot, Amazon Show, Google Home, and/or the like.Accordingly, the agent computing entity 103 may be configured to provideand/or receive information/data from a user via an input/outputmechanism, such as a display, a camera, a speaker, a voice-activatedinput, and/or the like. In certain embodiments, an AI computing entitymay comprise one or more predefined and executable program algorithmsstored within an onboard memory storage module, and/or accessible over anetwork. In various embodiments, the AI computing entity may beconfigured to retrieve and/or execute one or more of the predefinedprogram algorithms upon the occurrence of a predefined trigger event.

V. EXEMPLARY SYSTEM OPERATIONS

Various embodiments of the present invention provide techniques forincreasing efficiency and reliability of document processing systems bydynamically separating a batch of documents into processing groups basedat least in part on structural similarity of the pages of the noteddocuments. Absent this dynamic separation process, processing ofdocuments by processing agents (e.g., automated processing agents) maybe less efficient and less accurate, as the processing agents will beless likely to capture cross-temporal expertise acquired from repeatedlyprocessing input documents having similar structural formats. This inturn reduces the overall operational bandwidth and overall operationalreliability of a multi-agent distributed document processing system.Accordingly, by dynamically separating a batch of documents intoprocessing groups based at least in part on structural similarity of thepages of the noted documents, various embodiments of the presentinvention make important technical contributions to efficiency andreliability of document processing systems, and substantially improveoverall operational bandwidth and overall operational reliability ofexisting multi-agent distributed document processing systems.

FIG. 5 is a flowchart diagram of an example process 500 for performingdocument processing optimization. Via the various steps/operations ofthe process 500, the document processing optimization computing entity106 can efficiently and reliably separate a batch of input documentsinto processing groups based at least in part on layout similaritiesbetween input document pairs in the batch of input documents.

The process 500 begins at step/operation 501 when the documentprocessing optimization computing entity 106 identifies a plurality ofinput pages. Each input page of the plurality of input pages may beassociated with an input document of a plurality of input documents. Aninput document may be a data object that describes a collection ofcontent data including a collection of text data and/or a collection ofimage data. Examples of the input documents include PDF documents, TIFFdocuments, Microsoft Word files, and/or the like. Each input documentincludes one or more input pages. In some embodiments, each input pageof the plurality of input pages is a visually contagious unit of aninput document. Each input page of the plurality of input pages mayinclude one or more content segments. In some embodiments, each contentsegment is a visually contagious segment of an input page.

FIG. 6 depicts an operational example of a process for dividing a groupof documents into input pages. As depicted in FIG. 6, input document 601has been divided into input pages 602-604. In some embodiments, an inputpage includes one or more content segments which are of similar ordifferent types. As a non-limiting example, an input page may five pagesegments, including one picture subsegment, one graph subsegment, andthree separate word subsegments.

In some embodiments, the document processing optimization computingentity 106 receives the plurality of input documents from the clientcomputing entity 102. Receiving the plurality of input documents mayinvolve applying a time window to a stream of incoming input documents.The time window may be a data object that describe a time intervalduring which the document processing optimization computing entity 106receives the plurality of documents. Alternatively, receiving theplurality of input documents may involve applying a quantity window tothe stream of incoming documents. The quantity window may be a dataobjects that describes a number of input documents which the documentprocessing optimization computing entity 106 receives for processing.

At step/operation 502, the document processing optimization computingentity 106 processes each input page using a trained encoder model inorder to generate a fixed-dimensional representation of the input page.In some embodiments, the document processing optimization computingentity 106 processes each input page for an input document of theplurality of input documents using a trained encoder model in order togenerate a fixed-dimensional representation of the noted input page. Insome embodiments, the trained encoder model is a convolutional neuralnetwork configured to generate a fixed-dimensional representation foreach input page.

In some embodiments, step/operation 502 may be performed in relation toa particular input page of the plurality of input pages in accordancewith the process depicted in FIG. 7. The process depicted in FIG. 7begins at step/operation 701 when the document processing optimizationcomputing entity 106 generates a segmented page for the particular inputpage that describes one or more input page segments of the particularinput page. In some embodiments, in order to generate the segmented pagefor the particular input page document, the document processingoptimization computing entity 106 identifies one or more page segmentsin the particular input page, where each page segment of the one or morepage segments is associated with a corresponding group of pixels of theparticular input page. In some embodiments, each page segment includes agroup of pixels of the particular input page that have a common contentformat, e.g., a common textual format, a common pictorial format, acommon graphical format, and/or the like.

In some embodiments, the document processing optimization computingentity 106 identifies input page segments of the particular input pageby using a recursive page segmentation algorithm. A recursive pagesegmentation algorithm may be configured to start by identifying theentirety of the particular input page as being part of one input pagesegment, then proceed to iteratively identify smaller and smallersubsegments of the particular input page based at least in part onsubdividing subsegments identified in a previous iteration until astopping condition about similarity of data within an identified segmentis reached. After reaching the stopping condition, the recursive pagesegmentation algorithm may determine input page segments of theparticular input page based at least in part on the output of a finaliteration of the noted recursive page segmentation algorithm.

FIG. 8 provides an operational example of generating a segmented page822 for a particular input page 821. As shown in FIG. 8, the documentprocessing optimization computing entity 106 has identified various pagesegments of the input page 821 (e.g., page segments 801-803) and hasgenerated the segmented page 822 for the input page 821 based at leastin part on the various identified page segments of the input page 821.For example, the segmented page 822 includes the page segment 811 whichcorresponds to the page segment 801 of the input page 821. As anotherexample, the segmented page 822 includes the page segment 812 whichcorresponds to the page segment 802 of the input page 821. As yetanother example, the segmented page 822 includes the page segment 813which corresponds to the page segment 803 of the input page 821.

Returning to FIG. 7, at step/operation 702, the document processingoptimization computing entity 106 may optionally update the segmentedpage based at least in part on each content pixel density ratio for apage segment. The segmented page for the particular input page may beconfigured to describe each content pixel density ratio for a pagesegment in the particular input page as a grayscale value, where thegrayscale intensity of a page segment may describe the relativemagnitude of the content pixel density ratio for the pixel densityratio.

The content pixel density ratio may be a data object that describes aratio of pixels of a corresponding segment that is occupied by thecontent format associated with the page segment. For example, if a pagesegment is entirely occupied by a picture, it has a 100% content pixeldensity ratio. As another example, if a text-based page segment is 90%occupied by letter-depicting pixels and 10% occupied by whitespacepixels, it has a 90% pixel density ratio. In general, page segmentsdepicting smaller text typically are associated with higher contentpixel density ratio relative to page segments depicting larger text.Moreover, page segments depicting images typically are associated withhigher content pixel density ratio relative to page segments depictingtext content.

In some embodiments, to update the segmented page, the documentprocessing optimization computing entity 106 replaces each page segmentof the segmented page with a segment color, where the magnitude of thesegment color indicates a relative value of the content pixel densityratio for the page segment. For example, the document processingoptimization computing entity 106 replaces each page segment with agreyscale page segment. This page segment replacement may cause aclustering algorithm to ignore finer details, e.g., font style and size,of page segments that may vary widely across documents and instead focuson input page layout, which may be a better indicator of functionalsimilarity of documents for the purposes of work basket optimization ina document processing optimization system.

FIG. 9 provides an operational example of updating a segmented page byreplacing each page segment of the segmented page with agrayscale-valued box. As depicted in the grayscale-valued segmented page900 of FIG. 9, the grayscale value of each grayscale-valued boxdescribes the magnitude of the content pixel density ratio of the pagesegment associated with the grayscale-valued box. For example, the pagesegment associated with the grayscale-valued box 902 is deemed to have ahigher content pixel density ratio relative to both the page segmentassociated with the grayscale-valued box 901 and the page segmentassociated with the grayscale-valued box 901. As another example, thepage segment associated with the grayscale-valued box 901 is deemed tohave a higher content pixel density ratio relative to the page segmentassociated with the grayscale-valued box 903.

Returning to FIG. 7, at step/operation 703, the document processingoptimization computing entity 106 may optionally update the segmentedpage by performing down-sampling on the segmented page. In someembodiments, as part of the down-sampling, the dimensions of thesegmented page may be modified to a standard dimension for input pagesconfigured to be processed by the trained encoder model. In someembodiments, the document processing optimization computing entity 106may down-sample input documents by reducing a disparity between similargrayscale values, which may in turn enhance the semantic relevance offixed-dimensional representations generated based at least in part onprocessing the segmented pages and increase the effectiveness of thedocument clusters generated based at least in part on the notedfixed-dimensional representations. In some embodiments, step/operation703 may be performed instead of the step/operation 702. In someembodiments, step/operation 703 may be performed in addition to thestep/operation 702, e.g., subsequent to the step/operation 702 and/orprior to the step/operation 702.

At step/operation 704, the document processing optimization computingentity 106 generates the trained encoder model. A flowchart ofperforming generating the trained encoder model is depicted in FIG. 10.At step/operation 1001, the document processing optimization computingentity 106 determines a fixed-dimensional representation for eachtraining page of a plurality of training pages using an encoder model.In some embodiments, the document processing optimization computingentity 106 trains the encoder model using an encoder-decoderarchitecture. To that end, the document processing optimizationcomputing entity 106 feeds a first image to encoder. The encodergenerates a fixed-dimensional representation of the first image. Theencoder then uses the fixed-dimensional representation of the firstimage to generate a reconstructed first image. Afterwards, the documentprocessing optimization computing entity 106 compares the first image tothe reconstructed first image to determine an error measure and use theerror measure to set encoder parameters. A training page is a dataobject that describes a page that is configured to be used for trainingthe encoder-decoder model. A fixed-dimensional representation for acorresponding training page is a data object that describes a vectorgenerated by an encoder model after processing the correspondingtraining page based at least in part on the parameters of the encodermodel.

At step/operation 1002, the document processing optimization computingentity 106 processes each fixed-dimensional representation using adecoder model to generate a reconstructed page. The encoder modelencodes each input page into a fixed-dimensional representation. On theother hand, the decoder model is configured to generate a reconstructedpage for each training page based at least in part on thefixed-dimensional representation for the training page. Thereconstructed page has a similar dimension as the input page. Thereconstructed pages include a reconstructed page for each training pageof the plurality of training pages.

Afterwards, at step/operation 1003, the document processing optimizationcomputing entity 106 determines an error measure for the encoder modelbased at least in part on each reconstructed page. The error measure maybe determined based at least in part on a measure of deviation betweenthe reconstructed pages and their corresponding training pages.

At step/operation 1004, the document processing optimization computingentity 106 generates the trained error model based at least in part onthe error measure. In some embodiments, the document processingoptimization computing entity 106 generates the trained error modelbased at least in part on updating parameters of the encoder model inaccordance with the error measure. In some embodiments, the documentprocessing optimization computing entity 106 uses a gradient descentalgorithm to generate the trained error model in order to minimize basedat least in part on error measure.

At step/operation 705, the document processing optimization computingentity 106 processes each segmented page for an input page of theplurality of input pages using a trained encoder model to generate afixed-dimensional representation of the input page associated with thesegmented page. The fixed-dimensional representation for an input pagemay describe a vector generated by an encoder model after processing thecorresponding segmented page associated with the input page based atleast in part on the parameters of the encoder model. For example, asdepicted in FIG. 11, the trained autoencoder model 1102 generates abatch of fixed-dimensional representations 1103 that includes afixed-dimensional representation for each segmented page in a batch ofsegmented pages 1101.

Returning to FIG. 5, at step/operation 503, the document processingoptimization computing entity 106 determines a plurality of documentclusters based at least in part on each fixed-dimensional representationfor an input page of the plurality of input pages that are associatedwith the plurality of input documents. In some embodiments, eachdocument cluster of the plurality of document clusters includes a subsetof the plurality of documents that are deemed to be sufficiently similarbased at least in part on the fixed-dimensional representations of theplurality of input pages associated with the plurality of inputdocuments.

In some embodiments, step/operation 503 can be performed in accordancewith the process depicted in FIG. 12. The process depicted in FIG. 12begins at step/operation 1201 when the document processing optimizationcomputing entity 106 identifies a plurality of document pairs of aplurality of document pairs, where each document pair of the pluralityof document pairs includes a first document of the plurality of inputdocuments and a second document of the plurality of input documents.

At step/operation 1202, the document processing optimization computingentity 106 determines a cross-document similarity score for eachdocument pair based at least in part on each fixed-dimensionalrepresentation for an input page in a first subset of the plurality ofinput pages that is associated with the first document in the documentpair and each fixed-dimensional representation for an input page in asecond subset of the plurality of input pages that is associated withthe second document in the document pair. In other words, the documentprocessing optimization computing entity 106 determines thecross-document similarity score for a document pair based at least inpart on all of the fixed-dimensional representations associated with theinput pages of the two documents in the document pair. In someembodiments, to determine the cross-document similarity score for adocument pair, the document processing optimization computing entity 106uses a Jaccard similarity measure based at least in part on thecross-page similarity measures associated with input page pairs, whereeach input page pair comprises a first input page in a first document inthe document pair and a second input page in a second document in thedocument pair.

In some embodiments, to determine the cross-document similarity scorefor a document pair, the document processing optimization computingentity 106 first identifies a first subset of the plurality of the inputpages that includes all of the input pages that are part of the firstinput document in the document pair as well as a second subset of theplurality of the input pages that includes all of the input pages thatare part of the second input document in the document pair. Afterward,the document processing optimization computing entity 106 identifies aplurality of page pairs, where each page pair of the plurality of pagepairs includes a first input page from the first subset of the pluralityof input pages and a second input page from the second subset of theplurality of input pages. Next, the document processing optimizationcomputing entity 106 determines a cross-page similarity score for eachpage pair based at least in part on the fixed-dimensional representationfor the first input page in the page pair and the fixed-dimensionalrepresentation for the second input page in the page pair. Thereafter,the document processing optimization computing entity 106 determines asimilar-page count for the particular document pair based at least inpart on a count of the plurality of page pairs whose respectivecross-page similarity scores exceeds a cross-page similarity threshold;and determines the cross-document similarity score based at least inpart on the similar-page count and a total count of the plurality ofinput pages.

At step/operation 1203, the document processing optimization computingentity 106 determines the plurality of document clusters based at leastin part on each cross-document similarity score for a document pair ofthe plurality of document pairs. In some embodiments, the documentprocessing optimization computing entity 106 may use the pairwisedocument similarity scores with a clustering algorithm to generateclusters of similar documents. In some embodiments, the documentprocessing optimization computing entity 106 may cluster documents basedat least in part on the cross-document similarity scores for documentpairs. For example, the document processing optimization computingentity 106 may cluster a first document along with any documents whoserespective cross-document similarity score with respect to the firstdocument exceeds a cross-document similarity score threshold. In someembodiments, a density-based spatial clustering of applications withnoise (DBSCAN) algorithm may be used to determine the documents clustersbased at least in part on each cross-document similarity score for adocument pair of the plurality of document pairs.

Alternatively, the document processing optimization computing entity 106may determine the plurality of document clusters based at least in parton the page clusters. A flowchart diagram of an example process fordetermining a plurality of document clusters using page clusters isdepicted in FIG. 13. The process depicted in FIG. 13 begins atstep/operation 1301 when the document processing optimization computingentity 106 generates a reduced-dimensional representation for each inputpage of the plurality of input pages. In some embodiments, to generatethe reduced-dimensional representation for an input page, the documentprocessing optimization computing entity 106 performs t-distributedStochastic Neighbor Embedding (t-SNE) on the fixed-dimensionalrepresentation of the input page.

In some embodiments, the document processing optimization computingentity 106 reduces the input page representation dimension in a way thatseparates different page types. For example, the document processingoptimization computing entity 106 uses a t-SNE method to generatereduced-dimensional representation for each input pages. In someembodiments, the document processing optimization computing entity 106uses the t-SNE method to construct a probability distribution over pairsof high-dimensional objects in such a way that similar objects have ahigh probability of being picked while dissimilar points have anextremely small probability of being picked. Afterwards, the documentprocessing optimization computing entity 106 defines a similarprobability distribution over the points in the low-dimensional map, andminimizes a divergence between the two distributions with respect tolocations of the points in the map.

At step/operation 1302, the document processing optimization computingentity 106 determines a plurality of page clusters based at least inpart on each reduced-dimensional representation for an input page of theplurality of input pages. In some embodiments, the document processingoptimization computing entity 106 processes each reduced-dimensionalrepresentation for an input page of the plurality of input pages using aclustering algorithms to generate the plurality of page clusters, whereeach page cluster of the plurality of page clusters includes a relatedsubset of the plurality of input pages.

At step/operation 1303, the document processing optimization computingentity 106 determines the plurality of document clusters based at leastin part on the plurality of page clusters. As an example, given inputdocuments D1, D2, and D3, and determined page clusters C1 and C1, if D1has 80% of its pages in C1 20% of its pages in C2, and further if D2 has70% of its pages in C1 and 30% in C2, and further if D3 has 10% of itspages in C1 and 90% of its pages in C2, the document processingoptimization computing entity 106 may determine a document cluster DC1that includes D1 and D2, as well as a document cluster DC2 that includesD3.

In some embodiments, the document processing optimization computingentity 106 generates a document-cluster relatedness score for eachdocument-cluster pair of a plurality of document-cluster pairs thatcomprises an input document of the plurality of input documents and apage cluster of the plurality of page clusters and generates theplurality of document clusters based at least in part on eachdocument-cluster relatedness score for a document-cluster pair of theplurality of document-cluster pairs. A document-cluster relatednessscore for a document-cluster pair may be a data object that describes anestimated relevance of the document associated with the document-clusterpair and the page cluster associated with the document-cluster pair. Forexample, if 80% of the input pages associated with a first inputdocument fall within a first page cluster, the document processingoptimization computing entity 106 may determine a document-clusterrelatedness score of 0.8 for the document-cluster pair that includes thefirst input document and the first page cluster. In some embodiments,the document processing optimization computing entity 106 associateseach input document to a document cluster that includes all inputdocuments having a highest document-cluster relatedness score withrespect to a particular page cluster. For example, if an input documentD1 has a 0.1 document-cluster relatedness score with respect to a pagecluster C1, a 0.8 document-cluster relatedness score with respect to apage cluster C2, and a 0.1 document-cluster relatedness score withrespect to a page cluster C3, and further if an input document D2 has a0.3 document-cluster relatedness score with respect to a page clusterC1, a 0.4 document-cluster relatedness score with respect to a pagecluster C2, and a 0.3 document-cluster relatedness score with respect toa page cluster C3, the document processing optimization computing entity106 may assign both D1 and D2 to the same document cluster that includesinput documents having the highest document-cluster relatedness scorewith respect to the page cluster C2.

Returning to FIG. 5, at step/operation 504, the document processingoptimization computing entity 106 performs document processingoptimization based at least in part on the plurality of documentclusters. In some embodiments, to perform document processingoptimization, the document processing optimization computing entity 106determines a plurality of processing groups based at least in part onthe plurality of document clusters. In some embodiments, each processinggroup of the plurality of processing groups is associated with one ormore related document clusters of the plurality of document clusters. Insome embodiments, each processing group of the plurality of processinggroups comprises a subset of the plurality of input documents that isassociated with at least one of the one or more related documentclusters for the processing group. In some embodiments, each processinggroup of the plurality of processing groups is associated with anassigned processing agent (e.g., an assigned human agent, an assignedautomated agent, and/or the like) of a plurality of processing agents.

In some embodiments, step/operation 504 may be performed in accordancewith the process depicted in FIG. 14. The process depicted in FIG. 14begins at step/operation 1401 when the document processing optimizationcomputing entity 106 determines the plurality of processing groups basedat least in part on the document clusters. In some embodiments, eachdetermined processing group describes a document processing queueassociated with a corresponding processing agent. FIG. 15 provides anoperational example of determining processing groups. As shown in FIG.15, the document processing optimization computing entity 106 determinesthe processing groups 1502 based at least in part on the documentclusters 1501. For example, the processing group 1521 includes thedocument cluster 1511 and the document cluster 1512. As another example,the processing group 1522 includes the document cluster 1513 and thedocument cluster 1512.

At step/operation 1402, the document processing optimization computingentity 106 assigns each processing group to a processing agent. In someembodiments, each processing agent may be a data object that describesan automated agent and/or a manual agent configured to perform documentprocessing. In some embodiments, the document processing optimizationcomputing entity 106 assigns the processing group to processing agentsrandomly. Alternatively, in some embodiments, the document processingoptimization computing entity 106 assigns the processing group toprocessing agents based at least in part on cross-document-clustersimilarities scores between pairs of document clusters, such that eachprocessing agent is assigned document clusters with similar layoutsacross input documents of the noted document lusters. In someembodiments, the document processing optimization computing entity 106assigns the processing group to processing agents based at least in parton historical data associated with a particular processing agent,availability of processing agents, or past experience with a particularprocessing agent. In some embodiments, the document processingoptimization computing entity 106 may assign a particular processinggroup to more than one processing agents.

At step/operation 1403, the document processing optimization computingentity 106 causes the processing agents to perform document processing.In some embodiments, the document processing optimization computingentity 106 generates notifications for the processing agents that areconfigured to notify the processing agents of their relative documentprocessing queues. The document processing optimization computing entity106 may further update the work docket of the processing agents. In someembodiments, the document processing optimization computing entity 106updates a work user interface of the processing agent. In someembodiments, the document processing optimization entity 106 causes anautomated processing agent (e.g., a software program) to performdocument processing on the input documents that are in processing groupsassociated with the automated processing agent.

VI. CONCLUSION

Many modifications and other embodiments will come to mind to oneskilled in the art to which this disclosure pertains having the benefitof the teachings presented in the foregoing descriptions and theassociated drawings. Therefore, it is to be understood that thedisclosure is not to be limited to the specific embodiments disclosedand that modifications and other embodiments are intended to be includedwithin the scope of the appended claims. Although specific terms areemployed herein, they are used in a generic and descriptive sense onlyand not for purposes of limitation.

The invention claimed is:
 1. A computer-implemented method for documentprocessing optimization, the computer-implemented method comprising:identifying a plurality of input pages each associated with a relatedinput document of a plurality of input documents; for each input page ofthe plurality of input pages, generating a segmented page, whereingenerating the segmented page for a particular input page of theplurality of input pages comprises: identifying one or more pagesegments in the particular input page, wherein each page segment of theone or more page segments is associated with a relative location withinthe particular input page; for each page segment of the one or more pagesegments, determining a content pixel density ratio, wherein the contentpixel density ratio for the page segment is descriptive of a ratio ofpixels of the page segment that are occupied by a content formatassociated with the page segment; generating the segmented page as adata object that describes, for each page segment of the one or morepage segments, the relative location for the page segment and thecontent pixel density ratio for the page segment; and wherein thesegmented page for the particular input page is configured to describethe content pixel density ratio for the page segment as a grayscalevalue, and wherein generating the segmented page as the data objectcomprises: updating the segmented page by replacing the page segmentwith a segment color, wherein a magnitude of the segment color indicatesa relative value of the content pixel density ratio for the pagesegment; processing each segmented page for an input page of theplurality of input pages using a trained encoder model in order togenerate a fixed-dimensional representation of the input page;determining, based at least in part on each fixed-dimensionalrepresentation for an input page of the plurality of input pages, aplurality of document clusters, wherein each document cluster of theplurality of document clusters comprises a related subset of theplurality of documents; determining a plurality of processing groups,wherein: (i) each processing group of the plurality of processing groupsis associated with one or more related document clusters of theplurality of document clusters, (ii) each processing group of theplurality of processing groups comprises a subset of the plurality ofinput documents that is associated with at least one of the one or morerelated document clusters for the processing group, and (iii) eachprocessing group of the plurality of processing groups is associatedwith an assigned processing agent of a plurality of processing agents;and performing the document processing optimization based at least inpart on the plurality of processing groups.
 2. The computer-implementedmethod of claim 1, wherein generating the trained encoder modelcomprises: for each training page of a plurality of training pages,determining a training fixed-dimensional representation using an encodermodel; processing each training fixed-dimensional representation for atraining page of the plurality of training pages using a decoder modelin order to generate a plurality of reconstructed pages, wherein theplurality of reconstructed pages comprise a reconstructed page for eachtraining page of the plurality of training pages; determining an errormeasure for the encoder model based at least in part on the plurality oftraining pages and the plurality of reconstructed pages; and updatingparameters of the encoder model in accordance with the error measure inorder to generate the trained encoder model.
 3. The computer-implementedmethod of claim 1, wherein generating the segmented page of theparticular input page further comprises: subsequent to generating thesegmented page, updating the segmented page by performing down-samplingon the segmented page.
 4. The computer-implemented method of claim 1,wherein determining the plurality of document clusters comprises:identifying a plurality of document pairs, wherein each document pair ofthe plurality of document pairs comprises a first document of theplurality of input documents and a second document of the plurality ofinput documents; for each document pair of the plurality of documentpairs, determining a cross-document similarity score based at least inpart on each fixed-dimensional representation for an input page in afirst subset of the plurality of input pages that is associated with thefirst document in the document pair and each fixed-dimensionalrepresentation for an input page in a second subset of the plurality ofinput pages that is associated with the second document in the documentpair; and determining the plurality of document clusters based at leastin part on each cross-document similarity score for a document pair ofthe plurality of document pairs.
 5. The computer-implemented method ofclaim 4, wherein determining the cross-document similarity score for aparticular document pair of the plurality of document pairs comprises:for each page pair of a plurality of page pairs that comprises a firstinput page in the first subset associated with the particular documentpair and a second input page in the second subset associated with theparticular document pair, determining a cross-page similarity scorebased at least in part on the fixed-dimensional representation for thefirst input page in the page pair and the fixed-dimensionalrepresentational for the second input page in the page pair; determininga similar-page count for the particular document pair based at least inpart on a count of the plurality of page pairs whose respectivecross-page similarity scores exceeds a cross-page similarity threshold;and determining the cross-document similarity score based at least inpart on the similar-page count and a total count of the plurality ofinput pages.
 6. The computer-implemented method of claim 1, whereindetermining the plurality of document clusters comprises: performingt-distributed stochastic neighbor embedding on each fixed-dimensionalrepresentation for an input page of the plurality of input pages togenerate a reduced-dimensional representation for each input page of theplurality of input pages; clustering each reduced-dimensionalrepresentation for an input page of the plurality of input pages into aplurality of page clusters; for each document-cluster pair of aplurality of document-cluster pairs that comprises an input document ofthe plurality of input documents and a page cluster of the plurality ofpage clusters, generating a document-cluster relatedness score; andgenerating the plurality of document clusters based at least in part oneach document-cluster relatedness score for a document-cluster pair ofthe plurality of document-cluster pairs.
 7. The computer-implementedmethod of claim 1, wherein performing the document processingoptimization comprises: causing each processing agent of the pluralityof assigned agents to perform document processing on the processinggroup that is associated with the processing agent.
 8. Thecomputer-implemented method of claim 1, wherein identifying one or morepage segments in the particular input page comprises processing theparticular input page using a recursive page segmentation algorithm. 9.An apparatus for document processing optimization, the apparatuscomprising at least one processor and at least one memory includingprogram code, the at least one memory and the program code configuredto, with the processor, cause the apparatus to at least: identify aplurality of input pages each associated with a related input documentof a plurality of input documents; for each input page of the pluralityof input pages, generate a segmented page, wherein generating thesegmented page for a particular input page of the plurality of inputpages comprises: identifying one or more page segments in the particularinput page, wherein each page segment of the one or more page segmentsis associated with a relative location within the particular input page;for each page segment of the one or more page segments, determining acontent pixel density ratio, wherein the content pixel density ratio forthe page segment is descriptive of a ratio of pixels of the page segmentthat are occupied by a content format associated with the page segment;generating the segmented page as a data object that describes, for eachpage segment of the one or more page segments, the relative location forthe page segment and the content pixel density ratio for the pagesegment; and wherein the segmented page for the particular input page isconfigured to describe the content pixel density ratio for the pagesegment as a grayscale value, and wherein generating the segmented pageas the data object comprises: updating the segmented page by replacingthe page segment with a segment color, wherein a magnitude of thesegment color indicates a relative value of the content pixel densityratio for the page segment; process each segmented page for an inputpage of the plurality of input pages using a trained encoder model inorder to generate a fixed-dimensional representation of the input page;determine, based at least in part on each fixed-dimensionalrepresentation for an input page of the plurality of input pages, aplurality of document clusters, wherein each document cluster of theplurality of document clusters comprises a related subset of theplurality of documents; determine a plurality of processing groups,wherein: (i) each processing group of the plurality of processing groupsis associated with one or more related document clusters of theplurality of document clusters, (ii) each processing group of theplurality of processing groups comprises a subset of the plurality ofinput documents that is associated with at least one of the one or morerelated document clusters for the processing group, and (iii) eachprocessing group of the plurality of processing groups is associatedwith an assigned processing agent of a plurality of processing agents;and perform the document processing optimization based at least in parton the plurality of processing groups.
 10. The apparatus of claim 9,wherein generating the trained encoder model comprises: for eachtraining page of a plurality of training pages, determining a trainingfixed-dimensional representation using an encoder model; processing eachtraining fixed-dimensional representation for a training page of theplurality of training pages using a decoder model in order to generate aplurality of reconstructed pages, wherein the plurality of reconstructedpages comprise a reconstructed page for each training page of theplurality of training pages; determining an error measure for theencoder model based at least in part on the plurality of training pagesand the plurality of reconstructed pages; and updating parameters of theencoder model in accordance with the error measure in order to generatethe trained encoder model.
 11. The apparatus of claim 9, whereindetermining the plurality of document clusters comprises: identifying aplurality of document pairs, wherein each document pair of the pluralityof document pairs comprises a first document of the plurality of inputdocuments and a second document of the plurality of input documents; foreach document pair of the plurality of document pairs, determining across-document similarity score based at least in part on eachfixed-dimensional representation for an input page in a first subset ofthe plurality of input pages that is associated with the first documentin the document pair and each fixed-dimensional representation for aninput page in a second subset of the plurality of input pages that isassociated with the second document in the document pair; anddetermining the plurality of document clusters based at least in part oneach cross-document similarity score for a document pair of theplurality of document pairs.
 12. The apparatus of claim 11, whereindetermining the cross-document similarity score for a particulardocument pair of the plurality of document pairs comprises: for eachpage pair of a plurality of page pairs that comprises a first input pagein the first subset associated with the particular document pair and asecond input page in the second subset associated with the particulardocument pair, determining a cross-page similarity score based at leastin part on the fixed-dimensional representation for the first input pagein the page pair and the fixed-dimensional representational for thesecond input page in the page pair; determining a similar-page count forthe particular document pair based at least in part on a count of theplurality of page pairs whose respective cross-page similarity scoresexceeds a cross-page similarity threshold; and determining thecross-document similarity score based at least in part on thesimilar-page count and a total count of the plurality of input pages.13. The apparatus of claim 9, wherein determining the plurality ofdocument clusters comprises: performing t-distributed stochasticneighbor embedding on each fixed-dimensional representation for an inputpage of the plurality of input pages to generate a reduced-dimensionalrepresentation for each input page of the plurality of input pages;clustering each reduced-dimensional representation for an input page ofthe plurality of input pages into a plurality of page clusters; for eachdocument-cluster pair of a plurality of document-cluster pairs thatcomprises an input document of the plurality of input documents and apage cluster of the plurality of page clusters, generating adocument-cluster relatedness score; and generating the plurality ofdocument clusters based at least in part on each document-clusterrelatedness score for a document-cluster pair of the plurality ofdocument-cluster pairs.
 14. The apparatus of claim 9, wherein performingthe document processing optimization comprises: causing each processingagent of the plurality of assigned agents to perform document processingon the processing group that is associated with the processing agent.15. The apparatus of claim 9, wherein identifying one or more pagesegments in the particular input page comprises processing theparticular input page using a recursive page segmentation algorithm. 16.A computer program product for document processing optimization, thecomputer program product comprising at least one non-transitorycomputer-readable storage medium having computer-readable program codeportions stored therein, the computer-readable program code portionsconfigured to: identify a plurality of input pages each associated witha related input document of a plurality of input documents; for eachinput page of the plurality of input pages, generate a segmented page,wherein generating the segmented page for a particular input page of theplurality of input pages comprises: identifying one or more pagesegments in the particular input page, wherein each page segment of theone or more page segments is associated with a relative location withinthe particular input page; for each page segment of the one or more pagesegments, determining a content pixel density ratio, wherein the contentpixel density ratio for the page segment is descriptive of a ratio ofpixels of the page segment that is occupied by a content formatassociated with the page segment; generating the segmented page as adata object that describes, for each page segment of the one or morepage segments, the relative location for the page segment and thecontent pixel density ratio for the page segment; and wherein thesegmented page for the particular input page is configured to describethe content pixel density ratio for the page segment as a grayscalevalue, and wherein generating the segmented page as the data objectcomprises: updating the segmented page by replacing the page segmentwith a segment color, wherein a magnitude of the segment color indicatesa relative value of the content pixel density ratio for the pagesegment; process each segmented page for an input page of the pluralityof input pages using a trained encoder model in order to generate afixed-dimensional representation of the input page; determine, based atleast in part on each fixed-dimensional representation for an input pageof the plurality of input pages, a plurality of document clusters,wherein each document cluster of the plurality of document clusterscomprises a related subset of the plurality of documents; determine aplurality of processing groups, wherein: (i) each processing group ofthe plurality of processing groups is associated with one or morerelated document clusters of the plurality of document clusters, (ii)each processing group of the plurality of processing groups comprises asubset of the plurality of input documents that is associated with atleast one of the one or more related document clusters for theprocessing group, and (iii) each processing group of the plurality ofprocessing groups is associated with an assigned processing agent of aplurality of processing agents; and perform the document processingoptimization based at least in part on the plurality of processinggroups.
 17. The computer program product of claim 16, wherein generatingthe trained encoder model comprises: for each training page of aplurality of training pages, determining a training fixed-dimensionalrepresentation using an encoder model; processing each trainingfixed-dimensional representation for a training page of the plurality oftraining pages using a decoder model in order to generate a plurality ofreconstructed pages, wherein the plurality of reconstructed pagescomprise a reconstructed page for each training page of the plurality oftraining pages; determining an error measure for the encoder model basedat least in part on the plurality of training pages and the plurality ofreconstructed pages; and updating parameters of the encoder model inaccordance with the error measure in order to generate the trainedencoder model.
 18. The computer program product of claim 16, whereindetermining the plurality of document clusters comprises: identifying aplurality of document pairs, wherein each document pair of the pluralityof document pairs comprises a first document of the plurality of inputdocuments and a second document of the plurality of input documents; foreach document pair of the plurality of document pairs, determining across-document similarity score based at least in part on eachfixed-dimensional representation for an input page in a first subset ofthe plurality of input pages that is associated with the first documentin the document pair and each fixed-dimensional representation for aninput page in a second subset of the plurality of input pages that isassociated with the second document in the document pair; anddetermining the plurality of document clusters based at least in part oneach cross-document similarity score for a document pair of theplurality of document pairs.
 19. The computer program product of claim16, wherein determining the plurality of document clusters comprises:performing t-distributed stochastic neighbor embedding on eachfixed-dimensional representation for an input page of the plurality ofinput pages to generate a reduced-dimensional representation for eachinput page of the plurality of input pages; clustering eachreduced-dimensional representation for an input page of the plurality ofinput pages into a plurality of page clusters; for each document-clusterpair of a plurality of document-cluster pairs that comprises an inputdocument of the plurality of input documents and a page cluster of theplurality of page clusters, generating a document-cluster relatednessscore; and generating the plurality of document clusters based at leastin part on each document-cluster relatedness score for adocument-cluster pair of the plurality of document-cluster pairs.